Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("korvo/MS-RandomMergeTest_v2-22B")
model = AutoModelForCausalLM.from_pretrained("korvo/MS-RandomMergeTest_v2-22B")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))Quick Links
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the della merge method using concedo/Beepo-22B as a base.
Models Merged
The following models were included in the merge:
- TheDrummer/Cydonia-22B-v1.1
- anthracite-org/magnum-v4-22b
- crestf411/MS-sunfall-v0.7.0
- nbeerbower/Mistral-Small-Gutenberg-Doppel-22B
Configuration
The following YAML configuration was used to produce this model:
models:
- model: crestf411/MS-sunfall-v0.7.0
parameters:
weight: 0.35
density: 0.75
- model: nbeerbower/Mistral-Small-Gutenberg-Doppel-22B
parameters:
weight: 0.25
density: 0.55
- model: TheDrummer/Cydonia-22B-v1.1
parameters:
weight: 0.25
density: 0.45
- model: anthracite-org/magnum-v4-22b
parameters:
weight: 0.15
density: 0.45
merge_method: della
base_model: concedo/Beepo-22B
parameters:
int8_mask: true
normalize: true
epsilon: 0.05
lambda: 1.0
dtype: bfloat16
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="korvo/MS-RandomMergeTest_v2-22B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)